Validating Dose Uncertainty Estimates Produced by AUTODIRECT: An Automated Program to Evaluate Deformable Image Registration Accuracy.

Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm's performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student t distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool-produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated deformable image registration evaluation of confidence tool was also found to produce accurate confidence intervals for the dose-volume histograms of the deformed dose

Mainland Southeast Asia

The languages of Mainland South East Asia belong to five language phyla, yet they are often claimed to constitute a linguistic area. This chapter’s primary goal is to illustrate the areal features found in their prosodic systems while emphasizing their understated diversity. The first part of the chapter addresses the typology of word-level prosody. It describes common word shapes and stress patterns in the region, discusses tone inventories, and argues that beyond pitch, properties such as phonation and duration frequently play a role in patterns of tonal contrasts. The chapter next shows that complex tone alternations, although not typical, are attested in the area. The following section reviews evidence about prosodic phrasing in the area, discusses the substantial body of knowledge about intonation, and reconsiders the question of intonation in languages with complex tone paradigms and pervasive final particles. The chapter concludes with strategies for marking information structure and focus

Stressful events and adolescent psychopathology: A person-centred approach to expanding adverse childhood experience categories

Stress from cumulative adverse childhood experiences (ACEs) can pose a serious risk of experiencing anxiety, depression, and other mood disorders in adolescence. However, there is a paucity of research identifying specific profiles or combinations of exposure to other forms of stressful life events and their impact on adolescent psychopathology. This study attempted a conceptual expansion of the ACE checklist by examining these stressful events. The study used cross-sectional data from a modified version of the CASE Study survey where 864 adolescents (56% female, n = 480), aged from 11 – 18 years were recruited from four post-primary schools in the North-West region of NI. Latent class analysis of the 20-item stressful events checklist revealed 3 distinct risk classes: a low-risk class (53.5%), at-risk class (42.7%), and an immediate-risk class (3.8%). Results showed those at most risk of adolescent psychopathology had the highest probability of encountering interpersonal relationship issues, experiencing family dysfunction, and having close friends experiencing psychological difficulties. Findings indicate that the original ten ACE categories may be too narrow in focus and do not capture the wide range of childhood adversity. Expanding the ACE checklist to include other stressful events is discussed as these may also be antecedents to psychopathologic responses

Catalog of Chromium, Cobalt, and Nickel Abundances in Globular Clusters and Dwarf Galaxies

We present measurements of the abundances of chromium, cobalt, and nickel in 4113 red giants, including 2277 stars in globular clusters (GCs), 1820 stars in the Milky Way's dwarf satellite galaxies, and 16 field stars. We measured the abundances from mostly archival Keck/DEIMOS medium-resolution spectroscopy with a resolving power of R ~ 6500 and a wavelength range of approximately 6500–9000 Å. The abundances were determined by fitting spectral regions that contain absorption lines of the elements under consideration. We used estimates of temperature, surface gravity, and metallicity that we previously determined from the same spectra. We estimated systematic error by examining the dispersion of abundances within mono-metallic GCs. The median uncertainties for [Cr/Fe], [Co/Fe], and [Ni/Fe] are 0.20, 0.20, and 0.13, respectively. Finally, we validated our estimations of uncertainty through duplicate measurements, and we evaluated the accuracy and precision of our measurements through comparison to high-resolution spectroscopic measurements of the same stars

Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive

AbstractThe Quantitative Imaging Network (QIN), supported by the National Cancer Institute, is designed to promote research and development of quantitative imaging methods and candidate biomarkers for the measurement of tumor response in clinical trial settings. An integral aspect of the QIN mission is to facilitate collaborative activities that seek to develop best practices for the analysis of cancer imaging data. The QIN working groups and teams are developing new algorithms for image analysis and novel biomarkers for the assessment of response to therapy. To validate these algorithms and biomarkers and translate theminto clinical practice, algorithms need to be compared and evaluated on large and diverse data sets. Analysis competitions, or “challenges,” are being conducted within the QIN as a means to accomplish this goal. The QIN has demonstrated, through its leveraging of The Cancer Imaging Archive (TCIA), that data sharing of clinical images across multiple sites is feasible and that it can enable and support these challenges. In addition to Digital Imaging and Communications in Medicine (DICOM) imaging data, many TCIA collections provide linked clinical, pathology, and “ground truth” data generated by readers that could be used for further challenges. The TCIA-QIN partnership is a successful model that provides resources for multisite sharing of clinical imaging data and the implementation of challenges to support algorithm and biomarker validation

Multi-Test Tribometer

Final report and team photo for Project 21 of ME450, Fall 2009 semester.Reduced product lifetimes and increased energy use are two of the results of wear and friction. Testing the wear and friction behavior of components and materials is challenging. One common laboratory tool used to characterize wear is the pin-on-disk tribometer (a friction and wear tester) which places a pin (or fixed ball) on a rotating plate. A variation of this device is a linear reciprocating tribometer. The goal of this project is to develop a dual-mode tribometer that incorporates the ability to switch from one test mode to another, as well as measuring additional properties.Gordon Krauss (Mechanical Engineering, U of M)http://deepblue.lib.umich.edu/bitstream/2027.42/86211/1/ME450 Fall2009 Final Report - Project 21 - Multi-Test Tribometer.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/86211/2/ME450 Fall2009 Team Photo - Project 21 - Multi-Test Tribometer.jp

Preprocessing Among the Infalling Galaxy Population of EDisCS Clusters

We present results from a low-resolution spectroscopic survey for 21 galaxy clusters at $0.4 < z < 0.8$ selected from the ESO Distant Cluster Survey. We measured spectra using the low-dispersion prism in IMACS on the Magellan Baade telescope and calculate redshifts with an accuracy of $\sigma_z = 0.007$. We find 1763 galaxies that are brighter than $R = 22.9$ in the large-scale cluster environs. We identify the galaxies expected to be accreted by the clusters as they evolve to $z = 0$ using spherical infall models and find that $\sim30\%$ to $\sim70\%$ of the $z = 0$ cluster population lies outside the virial radius at $z \sim 0.6$. For analogous clusters at $z = 0$, we calculate that the ratio of galaxies that have fallen into the clusters since $z \sim 0.6$ to those that were already in the core at that redshift is typically between $\sim0.3$ and $1.5$. This wide range of ratios is due to intrinsic scatter and is not a function of velocity dispersion, so a variety of infall histories is to be expected for clusters with current velocity dispersions of $300 \lesssim\sigma\lesssim 1200$ km s$^{-1}$. Within the infall regions of $z \sim 0.6$ clusters, we find a larger red fraction of galaxies than in the field and greater clustering among red galaxies than blue. We interpret these findings as evidence of "preprocessing", where galaxies in denser local environments have their star formation rates affected prior to their aggregation into massive clusters, although the possibility of backsplash galaxies complicates the interpretation.Comment: Accepted for publication in Ap

Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS) 2013

International audienceBecause of their unpredictable appearance and shape, segmenting brain tumors from multi-modal imaging data is one of the most challenging tasks in medical image analysis. Although many different segmentation strategies have been proposed in the literature, it is hard to compare existing methods because the validation datasets that are used differ widely in terms of input data (structural MR contrasts; perfusion or diffusion data; ...), the type of lesion (primary or secondary tumors; solid or infiltratively growing), and the state of the disease (pre- or post-treatment). In order to gauge the current state-of-the-art in automated brain tumor segmentation and compare between different methods, we are organizing a Multimodal Brain Tumor Image Segmentation (BRATS) challenge that is held in conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013) on September 22nd, 2013 in Nagoya, Japan